An ensemble approach for research article classification: a case study in artificial intelligence
Min Lu, Lie Tang, Xianke Zhou · 2024
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Description
Min Lu, Lie Tang and Xianke Zhou wrote a 2024 study on sorting research papers by field, using AI papers as the test case. New fields blur into each other and shift fast, so keyword lists miss many papers. Their method blends several tools instead of one. A decision tree, a science-tuned language model and pattern matching each read distinct parts of a paper.
A further model merges the three outputs into one call. On hand-labeled data, the blend caught near all AI papers with high exactness. Its score beat the keyword method by a clear margin. Tests that removed one part at a time showed each part added value.
Catalog record
| CREATOR | Min Lu, Lie Tang, Xianke Zhou |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 60 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |